PSYC 2629 A01: Data-Based Decision Making

PSYC 2629 - Data-Based Decision Making

Summer 2026 Syllabus, Section A01, CRN 30693,

Credit hours: 3

Course Meeting Times

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Instructor

Matthew Lindberg

Professional Qualifications:
Ph.D. Experimental Psychology from The Ohio University (2010)
M.S. Experimental Psychology from The Ohio University (2007)
B.S. Psychology from the University of Florida (2002)
B.A. Criminology from the University of Florida (2002)

Email: mjlindberg@ysu.edu

Office: Beeghly Hall 4107

Office Phone: 330-941-1615

Preferred Contact Method: email

Communication Expectations:
I strive to respond to emails within 48 hours. If you do not receive a response within 48 hours, please send me another email.

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Course Description

2629. Data-Based Decision Making. Students will learn to make decisions and draw inferences from statistical analyses, with emphasis on hypothesis-testing and inferential techniques. Prereq.: C or better in both PSYC 2617 and PSYC 2618. 3 s.h.

Course Readings

Group Title Author ISBN
Required MindTap for Essentials of Statisticsfor the Behavioral Sciences Frederick J Gravetter; Larry B.Wallnau; Lori-Ann B. Forzano; JamesE. Witnauer 9780357585047

*First Day Access to MindTap and the eBook will be available through Blackboard. What is First Day? First Day is Barnes & Noble College's inclusiveaccess model, where digital course materials are included as an additional course charge for a particular course or program. This model is easy andconvenient for student use, provides an affordable option, and supports student success by ensuring every student is prepared for the first day ofclass.

First Day course materials are digital versions of the physical textbook that may include additional educational resources such as workbooks, problemsets, tutorials, video, simulations, and interactive software. Digital textbooks have many features that allow you to interact with your course contentlike never before. Depending on the course materials used, features may include highlighting, annotation, search functions, and multimedia links. AllFirst Day materials are easy to access through our Learning Management System.

The course readings are subject to change in the event of extenuating circumstances, research developments, current events, and/or to ensure better learning.  

Additional Course Materials

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Course Learning Outcomes/Objectives/Goals

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How to Succeed in This Course

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Attendance Expectations

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Late Work Submission Policy

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Additional Course Expectations

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Artificial Intelligence Policy Statement

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Assignments/Assessments

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Grading and Grading Scale

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University Policies

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Schedule of Topics and Assignments

Week of Reading(s) Proposed Topic Due/To Prepare for Class
6/29 Module 1: Chapter 8 Introduction to Hypothesis testing Lecture Annotation 8-1, 8-2, & 8-3 (Due Wednesday)
Respondus Lockdown Browser & Monitor practice (Due Wednesday)
Lecture Annotation 8-4, 8-5, & 8-6 (Due Thursday)
Mastery Training Ch8 (Due Sunday)
Problem Set Ch8 (Due Sunday)
Chapter Review Ch8 (Due Sunday)
Module 1 Exam (Due Sunday)
7/6 Module 2: Chapter 9 Introduction to the t-statistic Lecture Annotation 9-1 & 9-2 (Due Wednesday)
Lecture Annotation 9-3 & 9-4 (Due Thursday)
Mastery Training Ch9 (Due Sunday)
Problem Set Ch9 (Due Sunday)
Chapter Review Ch9 (Due Sunday)
SPSS Applied Activity Ch9 (A) (Due Sunday)
SPSS Applied Activity Ch9 (B) (Due Sunday)
Module 2 Exam (Due Sunday)
7/13 Module 3: Chapter 10 t-test for two independent samples Lecture Annotation 10-1, 10-2, & 10-3 (Due Wednesday)
Lecture Annotation 10-4 & 10-5 (Due Thursday)
Mastery Training Ch10 (Due Sunday)
Problem Set Ch10 (Due Sunday)
Chapter Review Ch10 (Due Sunday)
SPSS Applied Activity Ch10 (Due Sunday)
Module 3 Exam (Due Sunday)
7/20 Module 4: Chapter 11 t-test for two related samples Lecture Annotation 11-1, 11-2, & 11-3 (Due Wednesday)
Lecture Annotation 11-4 & 11-5 (Due Thursday)
Mastery Training Ch11 (Due Sunday)
Problem Set Ch11 (Due Sunday)
Chapter Review Ch11 (Due Sunday)
SPSS Applied Activity Ch11 (Due Sunday)
Module 4 Exam (Due Sunday)
7/27 Module 5: Chapter 12 Introduction to Analysis of Variance (ANOVA) Lecture Annotation 12-1, 12-2, & 12-3 (Due Wednesday)
Lecture Annotation 12-4, 12-5, & 12-6 (Due Thursday)
Mastery Training Ch12 (Due Sunday)
Problem Set Ch12 (Due Sunday)
Chapter Review Ch12 (Due Sunday)
SPSS Applied Activity Ch12 (Due Sunday)
Module 5 Exam (Due Sunday)
8/3 Module 6: Chapter 13 Two-factor Analysis of Variance Lecture Annotation 13-1 & 13-2 (Due Wednesday)
Lecture Annotation 13-3 (Due Thursday)
Mastery Training Ch13 (Due Sunday)
Problem Set Ch13 (Due Sunday)
Chapter Review Ch13 (Due Sunday)
SPSS Applied Activity Ch13 (Due Sunday)
Module 6 Exam (Due Sunday)
8/10 Module 7: Chapter 14 Correlation & Regression Lecture Annotation 14-1, 14-2, & 14-3 (Due Wednesday)
Lecture Annotation 14-4, 14-5, & 14-6 (Due Thursday)
Mastery Training Ch14 (Due Friday)
Problem Set Ch14 (Due Friday)
Chapter Review Ch14 (Due Friday)
SPSS Applied Activity Ch14 (Due Friday)
Module 7 Exam (Due Friday)

The course schedule, policies, procedures, and assignments in this course are subject to change in the event of extenuating circumstances, by mutual agreement, and/or to ensure better learning.